Machine Learning Deep Learning model deployment

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Price: 19.99$

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques. This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples Course Structure: Creating a Classification Model using Scikit-learn Saving the Model and the standard Scaler Exporting the Model to another environment – Local and Google Colab Creating a REST API using Python Flask and using it locally Creating a Machine Learning REST API on a Cloud virtual server Creating a Serverless Machine Learning REST API using Cloud Functions Building and Deploying Tensor Flow and Keras models using Tensor Flow Serving Building and Deploying Py Torch Models Converting a Py Torch model to Tensor Flow format using ONNXCreating REST API for Pytorch and Tensor Flow Models Deploying tf-idf and text classifier models for Twitter sentiment analysis Deploying models using Tensor Flow. js and Java Script Tracking Model training experiments and deployment with MLFLow Running MLFlow on Colab and Databricks Python basics and Machine Learning model building with Scikit-learn will be covered in this course. This course is designed for beginners with no prior experience in Machine Learning and Deep Learning You will also learn how to build and deploy a Neural Network using Tensor Flow Keras and Py Torch. Google Cloud (GCP) free trial account is required to try out some of the labs designed for cloud environment.